The Role of Oceanic Transform Faults in SeafloorSpreading: A Global Perspective FromSeismic AnisotropyCaroline M. Eakin1,2 , Catherine A. Rychert2 , and Nicholas Harmon2

1Research School of Earth Sciences, The Australian National University, Canberra, ACT, Australia, 2Ocean and Earth Science,National Oceanography Centre Southampton, University of Southampton, Southampton, UK

Abstract Mantle anisotropy beneath mid-ocean ridges and oceanic transforms is key to ourunderstanding of seafloor spreading and underlying dynamics of divergent plate boundaries. Observationsare sparse, however, given the remoteness of the oceans and the difficulties of seismic instrumentation. Toovercome this, we utilize the global distribution of seismicity along transform faults to measure shearwave splitting of over 550 direct S phases recorded at 56 carefully selected seismic stations worldwide.Applying this source-side splitting technique allows for characterization of the upper mantle seismicanisotropy, and therefore the pattern of mantle flow, directly beneath seismically active transform faults.The majority of the results (60%) return nulls (no splitting), while the non-null measurements display clearazimuthal dependency. This is best simply explained by anisotropy with a near vertical symmetry axis,consistent with mantle upwelling beneath oceanic transforms as suggested by numerical models. It appearstherefore that the long-term stability of seafloor spreading may be associated with widespread mantleupwelling beneath the transforms creating warm and weak faults that localize strain to the plate boundary.

1. Introduction

Most of Earth’s crust, both present and in the past, was formed along the global mid-ocean ridge (MOR)system where two oceanic plates are pulled apart. A fundamental feature of this seafloor spreading is theformation of transform faults of varying length that offset the ridge segments at 90°. This characteristicridge-transform geometry is a key component of plate tectonics and governs the creation of new seafloor(Wilson, 1965). Despite the fundamental role of oceanic transform faults, tight constraints on the underlyingdynamics have proven challenging due to the inaccessibility of the oceans. Given that transform faults areabsent during continental rifting (e.g., the East African Rift) (Pagli et al., 2015), it is unclear why and whentransform faults initiate, or how they are maintained over time. The implication is for zones of weakness inthe lithosphere upon which strain is localized to ensure long-term stability of the plate boundary (Gerya,2012). Elevated levels of aseismic slip, or rather a seismic deficit, also points toward particularly weak faults(Abercrombie & Ekstrom, 2001).

Deformation of the upper mantle is often associated with the development of seismic anisotropy. Plateboundaries, where strain is concentrated, are therefore expected to display strong anisotropic signatures(i.e., directional dependence of seismic velocity). Such anisotropy forms as a result of mantle deformationin the dislocation creep regime (Karato, 2008). This causes a rotation and alignment of individual olivinecrystals, of which the upper mantle is mostly composed, producing what is known as a lattice-preferred orien-tation (LPO) (Christensen, 1984; Nicolas & Christensen, 1987). By investigating the properties of seismic wavesas they pass through the upper mantle, it is therefore possible to deduce the pattern of mantle flow if the rela-tionship between strain geometry and the resulting crystallographic orientation is known. For olivine the typeof LPO that develops is dependent on physical and chemical conditions present, such as water content andtemperature (Jung et al., 2006; Jung & Karato, 2001; Katayama et al., 2004). Under typical upper mantle con-ditions A-, C-, or E-type olivine fabrics are expected for which the fast direction, as measured by teleseismicshear waves, is expected to align with the mantle flow direction (Karato et al., 2008; Zhang & Karato, 1995).

Alternatively, seismic anisotropy can also be generated according to a shape-preferred orientation fromlayering between two materials of different seismic properties, for example, aligned partial melt (Holtzmanet al., 2003). In this case seismic waves travel slowest normal to the layering and fastest in any direction

EAKIN ET AL. 1

PUBLICATIONSJournal of Geophysical Research: Solid Earth

RESEARCH ARTICLE10.1002/2017JB015176

Key Points:• Seismic anisotropy beneath oceanictransforms is revealed by globalsource-side splitting analysis

• Results are characterized by nulls(60%) and azimuthally dependentsplitting

• The pattern found is consistent withwidespread mantle upwelling assuggested by numerical models

Supporting Information:• Supporting Information S1• Data Set S1• Data Set S2

Correspondence to:C. M. Eakin,caroline.eakin@anu.edu.au

Citation:Eakin, C. M., Rychert, C. A., & Harmon, N.(2018). The role of oceanic transformfaults in seafloor spreading: A globalperspective from seismic anisotropy.Journal of Geophysical Research: SolidEarth, 123. https://doi.org/10.1002/2017JB015176

Received 30 OCT 2017Accepted 3 FEB 2018Accepted article online 8 FEB 2018

©2018. The Authors.This is an open access article under theterms of the Creative CommonsAttribution License, which permits use,distribution and reproduction in anymedium, provided the original work isproperly cited.

parallel to layering (i.e., transverse isotropy). This type of seismic aniso-tropy is thought to be prevalent in the shallow crust due to thealignment of stress-induced cracks and fractures (Crampin, 1994).

Characterizing seismic anisotropy beneath oceanic transforms thereforeholds the potential to inform us about the underlying mantle dynamics,distribution of any melt, and the presence of other highly anisotropicminerals such as hydrous phases. Seismic observations over the oceans,particularly of the plate boundaries, are sparse given the difficulty andexpense of deploying ocean bottom seismometers (OBS). Some of theearliest studies of seismic anisotropy from the oceanic realm came fromseismic refraction surveys (Pn studies), which showed that the upper-

most mantle, just below the Moho, was anisotropic with a fast direction parallel to the paleo-spreading direc-tion (Gaherty et al., 2004; Hess, 1964; Raitt et al., 1969). More broadly, the global pattern of azimuthalanisotropy for the oceanic upper mantle can be described from surface wave observations (e.g., Begheinet al., 2014; Debayle & Ricard, 2013; Schaeffer et al., 2016). Generally these show an alignment of the fastdirection with the absolute plate motion in the asthenosphere, and the paleo-spreading direction in thelithosphere. While surface waves are useful for retrieving information about depth dependency, they tendto average laterally and therefore are not well suited to resolving in detail the plate boundaries.

Arguably, the best method to make detailed point-based measurements of seismic anisotropy at the plateboundary is with shear wave splitting. When a shear wave enters an anisotropic medium, such as the uppermantle, it is split into two effectively orthogonal polarisations, a phenomenon equivalent to crystallographicbirefringence. These two polarisations correspond to a fast (Φ) and a slow orientation and accumulate a delaytime (δt) between them due to their difference in seismic wave speed. The magnitude of the delay timedepends upon the strength of the anisotropy and the path length through the anisotropic domain. For thesame anisotropic domain, the path length, and therefore delay time, may vary for different shear wavephases with different angles of incidence.

Typically, mantle anisotropy beneath a seismic station is derived using teleseismic phases such as SKS(Figure 1). These travel through the outer core as a P wave, removing splitting accrued on the downward-leg beneath the earthquake source, and polarizing the upward traveling S wave as it emerges from the outercore into the source-receiver plane (i.e., aligned with the back azimuth). The eventual SKS splitting recorded isthus accumulated between the lowermost mantle and the surface beneath the receiver. It is thought thatmost of the lower mantle is isotropic and that anisotropy primarily resides in the upper mantle, and possiblyto a lesser degree in the transition zone (most likely in the vicinity of subducting slabs) (Auer et al., 2014;Chang et al., 2015; French & Romanowicz, 2014; Moulik & Ekstrom, 2014). The lowermost layer of the mantle(D″) is also known to be anisotropic (Kendall & Silver, 1996; Montagner, 1998; Nowacki et al., 2011), but thepath length for SKS is relatively short compared to the upper mantle.

The continents have been blanketed by such SKS splitting measurements (http://splitting.gm.univ-montp2.fr/DB) (Wüstefeld et al., 2009) but the ocean basins and mid ocean ridge - transform fault (MOR-TF) systemremain mostly blank except for a small handful of studies. The MELT (Wolfe & Solomon, 1998) and GLIMPSE(Harmon et al., 2004) experiments traversed a relatively straight and fast spreading ridge segment on theEast Pacific Rise (around 114°W, 15°S) and found fast directions subparallel to the spreading direction.Likewise for the Cascadia Initiative, which covered the entire Juan de Fuca plate from ridge to trench, fastdirections across the plate and its boundaries were found to align with the large-scale plate motion(Bodmer et al., 2015; Martin-Short et al., 2015).

Instead of measuring seismic anisotropy beneath the seismic station, the source-side technique can beemployed to measure anisotropy beneath the earthquake source (Russo & Silver, 1994)(Figure 1). Such atechnique has been successfully deployed in numerous subduction settings around the world (Eakin et al.,2016; Eakin & Long, 2013; Foley & Long, 2011; Lynner & Long, 2013, 2014; Russo, 2009; Russo et al., 2010)but has scarcely been applied to other types of plate boundaries (Nowacki et al., 2012). In this technique split-ting of teleseismic S phases are measured at seismic stations for which the anisotropy beneath the receiver iswell known from SKS analysis and can be corrected for (or neglected in the case of isotropy). If the lower man-tle is mainly isotropic then the remaining splitting on the direct S wave should be attributable to anisotropy

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Figure 1. Schematic raypath geometry of the source-side splittingmethod. Ifseismic anisotropy beneath the receiver can be neglected (no splitting of SKSphases), and the lower mantle is isotropic, then any splitting of direct Sphases as shown is attributable to seismic anisotropy in the upper mantlebeneath the earthquake source.

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beneath the earthquake source, hence the term “source-side.” Event-station pairs in the 40°–80° epicentraldistance range are used for this type of analysis to maintain a relatively steep angle of incidence while avoid-ing passage through the D″ region.

Recently, Nowacki et al. (2012) conducted the first source-side splitting study using MOR earthquakes (mostlyon the Mid-Atlantic and East Pacific Ridges) using stations in North America and East Africa. The studyfocused on ridge events and found that fast directions are subparallel to plate motion away from the spread-ing center, but closer to the ridge axis fast directions become more variable and splitting times decrease.Azimuthal dependence was identified for two events on the Mid-Atlantic Ridge, where the fast direction dif-fered betweenmeasurements made in North America versus Africa. The limited station distribution, however,restricted further exploration across a broader azimuthal range.

In the present study we conduct a new source-side (direct S) splitting analysis with a sevenfold increase inmeasurements using a global network of suitable seismic stations. This provides worldwide coverage ofthe entire MOR-TF system (subject only to seismicity), sampling the seismically active oceanic transform faultsparticularly well. Using our global station distribution, the azimuthal dependence of seismic anisotropybeneath transform faults is characterized and modeled on the global scale.

2. Data and Methods2.1. Station Selection

For source-side measurements, as we conduct here, the largest potential source of error is incorrect charac-terization of the anisotropy beneath the seismic station. For this reason careful station selection is the mostimportant step in the process. Given the restricted distribution of earthquakes in the world, it is usually diffi-cult to record SKS arrivals across a wide range of back azimuths, which is critical for conclusively determiningthe anisotropic structure, for example, single layered or multilayered (Rümpker & Silver, 1998; Silver & Savage,1994). To best circumvent this complication in this study, we limit ourselves to only null stations. These arestations for which SKS splitting analysis has returned an overwhelming majority of nulls (i.e., a clear SKS pulsethat has not undergone splitting) across a substantial swath of back azimuths. This indicates that the uppermantle beneath the station is effectively isotropic to shear waves with a steep angle of incidence. An initialcatalogue of 83 such null stations was compiled from a range of previous studies (Eakin et al., 2015; Foley& Long, 2011; Long, 2010; Lynner & Long, 2013, 2014, 2015; Paul & Eakin, 2017; Walpole et al., 2014). A full listis provided in Table S1 of the supporting information.2.1.1. Automated SKS AnalysisTo ensure the reliability of these null stations, we conducted our own SKS splitting analysis using an auto-mated approach for speed and efficiency. For each station we selected events of magnitude >6.0 and inthe distance range 88°–130° on which to analyze SKS splitting. All seismograms were band-pass filteredbetween 0.04 and 0.125 Hz. The splitting analysis was performed using the standard SplitLab softwarepackage (Wüstefeld et al., 2008). We use the original version of the program SplitLab 1.0.5 in which the errorestimation has not beenmodified according to (Walsh et al., 2013). Typically, the timewindow around the SKSphase is hand-picked and varied to obtain the best result. In order to speed up the calculation for many thou-sands of SKS events we adapted the code to eliminate the visual inspection routine and instead fixed the timewindow to ±15 s on either side of the predicted SKS arrival time. The signal-to-noise ratio using this timewindow was computed, and events with signal-to-noise ratio < 5.0 were discarded. This simple automationtechnique cannot reproduce the accuracy or detail of visually inspecting each seismogram, particularly forcomplex anisotropic structures (e.g., Eakin & Long, 2013), but if the stations are indeed characteristically nullas previously published, then that should be unquestionably clear with a simplified approach.

Within the SplitLab environment two independent measurement methods are applied over a grid search todetermine the predicted fast direction (Φ) and delay time (δt). These two approaches are the minimumenergy method (Silver & Chan, 1991) denoted by SC, and the rotation correlation method (Bowman &Ando, 1987) denoted by RC. A comparison of the predicted splitting parameters (Φ and δt) returned by thetwo different methods provides a simple diagnostic tool for classifying splits and nulls as outlined byWüstefeld & Bokelmann (2007). If anisotropy is present, then the two methods should predict similar splittingparameters. For a null, however, the RC method tends toward a delay time of zero and a systematic deviationof ΦRC by 45°. This results in a delay time ratio (δtRC/δtSC) close to unity for a split and close to zero for a null.

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Additionally, the angular difference (ΔΦ) between predicted fast directions (ΦSC � ΦRC) tends to zero for asplit and toward 45° for a null. An example of this classification system for station CBKS is shown in FigureS1. A station was dropped from our list if the percentage of “good” or “fair” nulls was less than 80% of thetotal (splits and nulls). The average total number of measurements per station was 205.

We also assessed the results as a function of back azimuth to check for consistency (Figure S2). We requirethat nulls are not just found at one particular back azimuth but instead fall over a wide swath (minimum50°). This ensures that the upper mantle structure below the station is apparently isotropic to all such phasesand not just the result of back azimuth alignment with a fast or slow direction, which would have a clearlyidentifiable 90° periodicity. Following this inspection 24 stations were cut from our list, leaving us with 56 nullstations with robust apparent mantle isotropy below (Table S1 and Figure 2). The stations that were notredeemed robust may have implications for previous source-side studies.2.1.2. Station MisalignmentWhen making accurate shear wave splitting measurements, another potentially significant source of error isthe orientation of the seismic station (Tian et al., 2011). Previous studies have shown that the reportedazimuth of the horizontal components can be off by 10° or more due to the difficulty of orientating a seism-ometer in the field (e.g., Ekstrom & Busby, 2008). As it so happens analysis of SKS polarization provides analternative method for calculating the station orientation (Vidale, 1986). Due to the polarization effect oftraveling as a P wave in the outer core, SKS phases are initially aligned to the back azimuth. By observingthe horizontal particle motion of SKS phases and comparing to the known source-receiver back azimuth,the station misalignment can be determined (Figure 3a).

We included this procedure as part of our automated SKS analysis. This was achieved by measuring the angleof the first eigenvector (longest axis) of the SKS particle motion from the north and east components. If theSKS phase is not split (i.e., null), as most events were, then the initial particle motion is linear and the orienta-tion of the first eigenvector is well defined. Even in the case of splitting, the initial particle would be elliptical,with the long axis of the ellipse aligned with the back azimuth.

From this type of analysis, it was found that for the majority of stations, the average misalignment angle isclose to zero (Figure 3b); that is, the correct station orientation is known. Upon closer inspection, however,it was noticed that for some stations estimates of the misalignment angle vary substantially. When these sta-tions are plotted as a function of time (Figure 3c), a step can usually be seen where the misalignment sud-denly changes. It is likely that the seismometer was moved at this point in time during instrumentservicing. In most cases the misalignment improves suggesting that a known problem was being fixed. Atable of all the stations used in this study and their misalignment values are provided in Table S1 for futurereference. Using these values, a time-dependent correction was applied to the stations before subsequentsource-side splitting analyses were made.

Figure 2. Map of seismic stations (blue triangles) and oceanic transform events (green circles) used in this study. A full list isprovided in the supporting information. Plate boundaries (dashed red line) from Bird (2003).

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2.2. Source-Side Analysis

Following the steps outlined previously, we are left with 56 null stations distributed around the world(Figure 2) that are reliable, and in the correct orientation, ready for source-side splitting analysis to be under-taken. Using these stations, we search for suitable earthquakes of magnitude 5.5 and above in the epicentraldistance range 40°–80° from each station. This returns 1,337 individual events spanning the entire globalMOR-TF system, many of which are recorded across multiple stations and locations (each event recordedby 3.2 stations on average). For the purposes of this study we focus on 995 of the events (74% of the dataset) with strike-slip source mechanisms (Ekström et al., 2012) associated with oceanic transform faults(Figure 2). Results relating to the remainder of the events can be found in the supporting information.

Using these stations, we measured shear-wave splitting on the direct S phase in a manner similar to thatdescribed earlier for SKS analysis (section 2.1.1). The same two methods, SC and RC, are applied throughSplitLab, and the waveforms are analyzed in the same frequency band (0.04–0.125 Hz) to negate any compli-cations associated with frequency dependence. For the SCmethod, the splitting parameters are estimated byminimizing the energy on the component orthogonal to the initial polarization direction (i.e., the polarizationof the shear wave before it encounters anisotropy). Unlike for SKS phases in which the initial polarization isknown, for direct S phases, the initial polarization requires calculation. This we estimate from the long axis

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Figure 3. Estimating seismic station orientation from SKS initial polarization. (a) Difference between the uncorrected SKSparticle motion (north versus east components) and the source-receiver back azimuth reveals the misalignment angle.(b) Histogram of the average misalignment estimated from all SKS events at each station. Most stations appear correctlyorientated (misalignment = 0°), but misalignment errors of ±15° are not unusual. (c) Example of misalignment estimates asa function of time for station CBN. The black dashed line is the moving average. Orientation of the seismometer appears tochange in mid-2007. Corrections to station orientations are therefore applied as a function of time. Misalignments forother stations are provided in the supporting information.

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of the ellipse in the uncorrected particle motion, which preserves the initial polarization direction whensplitting times are small relative to the dominant period of the S phase (Eakin & Long, 2013). A comparisonbetween this method and others for estimating the initial polarization is shown in Figure S3 (Marson-Pidgeon & Savage, 2004; Wolfe & Silver, 1998). While both the RC and SC methods are used forcomparison and quality control, henceforth, the reported splitting parameters are from the RC method asit is independent of the initial polarization.

Previously, for SKS analysis, the process was automated for speed, but for the source-side measurements, wewish to be as careful as possible so we revert to visual inspection of every seismogram for accuracy. Thisallows for several additional quality measures to be implemented for source-side splitting measurements(Figure S4). Namely, a characteristic shear wave pulse must be clearly visible above the noise on both horizon-tal components (rotated with respect to the initial polarization), both with a similar shape but separated by asmall time delay. The component normal to the initial polarization should be flat (i.e., energy minimal) follow-ing correction for splitting, and the corrected particle motion linearized in the initial polarization direction.The uncorrected particle motion should be elliptical, not circular, to ensure that the small delay time

Figure 4. Source-side splitting results from global oceanic transform earthquakes. Results are plotted at the source loca-tion. For the upper map, the number of null measurements (per event) is shown by a colored cross according to thecolor scale given. Nonnull measurements, that is, splits (colored bars), are shown on the lower map. The orientation ofthe bar represents the measured fast direction, and its length is scaled by the delay time found. An example for 2 s is givenin the legend. The bars are colored based on the azimuth of the raypath (azimuth from the source pointing toward receiver)according to the color wheel provided.

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approximation holds for calculating the initial polarization. The error regions for the estimated splitting para-meters were required to be circular and relatively small (0.5 s in δt and 22.5° inΦ) at the 95% confidence level.A similar degree of agreement, that is, within this standard error range, was required between the separateRC and SC estimates. In the case of a null source-side result, a clear shear wave pulse should be visible on thecomponent parallel to the initial polarization but not on the perpendicular component, producing linearuncorrected particle motion.

Finally, anisotropy beneath the source is sensed by downgoing rays, but splitting is measured at the stationfrom upgoing rays. Due to this difference in the frame of reference (upgoing versus downgoing), the fastdirections need to be reflected about the great circle path (i.e., azimuth) to project back to the true orienta-tion beneath the source.

3. Results

Our analysis yielded 556 source-side measurements from 367 transform events (Table S2), which we plot attheir event locations in Figure 4. The majority of the results (60%, 332/556) were null observations indicatingthat these shear waves did not undergo any splitting. The remaining 40% (224 out of 556) did show splitting.Of these split results, the mean delay time is 1.7 s (mean error ±0.4 s), which is fairly substantial. However,their fast directions (orientation of colored bars in Figure 4b) are variable, showing no clear trend, even atindividual locations. Most stations (49 out of 56) recorded both null and split observations, indicating thatthe predominance of null results is not a receiver-side effect. The characteristics are similar when ridge eventsare also included in the analysis (Table S2 and Figure S5).

When compared against the spreading direction, the distribution of fast orientations from transform eventsappears uniform, with no clear preference for spreading parallel or spreading normal, that is, ridge-parallelorientations (Figure 5a). This holds true even as a function of distance from the spreading center. Theprobability of splits versus nulls is also unaffected by the distance from the spreading ridge (Figures 5eand 5g); instead, both are tied to the available distribution of seismicity and are equally likely to occur atany set distance. There is little change in average observed delay times with distance from the ridge axiseither (Figure 5c). The same is true when making comparisons with the spreading rate (Figures 5b, 5d, 5f,and 5h). Delay times and the preponderance of splits versus nulls are similar for both fast spreading ridgesand slow spreading ridges. There therefore appears to be little difference in the general anisotropic charac-teristics beneath fast versus slow transforms.

3.1. Comparison With SKS Studies

Where we have SKS splitting measurements from broadband OBS deployments, we can compare with ourresults. Only a handful of such experiments near oceanic transforms have ever been conducted, which inpart motivated this study. Results from the most extensive deployment to date, the Cascadia Initiative,covering the entire Gorda-Juan de Fuca plate from ridge to trench, are shown in Figure 6 (Bodmer et al.,2015; Martin-Short et al., 2015). In this region, our two new source-side splitting measurements (in orange)return very similar splitting characteristics, in terms of both fast direction and delay time (the orientationand size of the bar), compared to the nearby SKS results (Figures 6a and 6b). This confirms that thesource-side method we have employed is correctly capturing the anisotropic properties beneath theearthquake source.

We also record seven null measurements in the region, near the Blanco Transform Fault (Figure 6c). Bodmeret al. (2015) did not measure nulls as part of their study, so we are only able to compare with the null resultsfromMartin-Short et al. (2015). For the three stations closest to the Blanco Transform Fault, and to our source-side events (stars), multiple null results are recorded at each station (orange to red circles). Across the deploy-ment as a whole, most stations record zero or one null SKS measurement (no circle or yellow circle). Therelative number of splits at the three closest stations also appears reduced (Figure 6b), with two stationshaving only 1 splitting measurement, and the third having three. Near to the transform fault, the generalcharacteristics of our source-side results therefore appear to be in agreement with the individual SKS resultswith a greater tendency for nulls and a similarity in the more limited splitting. For the Bodmer et al. (2015)study only the stacked splitting results are available, but we do note that the number of events used to build

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Figure 5. Distribution of results for transform fault events as a function of distance from the closest spreading ridge (leftcolumn) and spreading rate (right column) from (Müller et al., 2008). The left column is equivalent to distance along thetransform, with the distance to the nearest ridge segment from (Bird, 2003) chosen. (a and b) The absolute angular dif-ference between the measured fast direction (Φ) and the local orientation of seafloor spreading from NUVEL1A (DeMetset al., 1994). (c and d) The distribution of measured delay times. The mean value (1.8 s) is plotted as a dotted line. (e and f)The percentage of splits (as a fraction of the total number of splits) that occurs within a given bin. (g and h) The same fornulls. The pattern as a function of distance and spreading rate is similar for both split and null measurements.

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the stack is on average less for the “Blanco” region (3.4 events per station; 32 stations total), compared to therest of the data set (5.5 events on average across 84 stations).

The only other location on an oceanic transform fault where there are SKS measurements with which to com-pare is in southern Iceland. Given the anomalous tectonic setting, with likely plume interactions at play, wediscuss these results in Figure S6 (Stefánsson et al., 1993; Xue & Allen, 2005). We note, however, that again thesplitting characteristics are consistent between source-side and SKS splitting methods.

3.2. Azimuthal Dependence

Given our global network of null stations (Figure 2), we were often able to measure source-side splitting fromthe same source location across multiple stations in different parts of the world. In 25 different locations wehave four or more source-side splitting measurements for the same event or event cluster (closely spacedevents separated by less than 1°) (Figure S7). This allowed us to consider and discover the presence of azi-muthal dependency in our results. We find that the variability in fast directions seen in the splitting results(Figure 4b) appears related to the azimuth of the raypath between the event and the station. When we plotthe splitting results and color-code them by azimuth (Figures 4b and 6), we find that in a given location simi-lar azimuths (i.e., similar colors) tend to produce similar splitting characteristics. Conversely, when measure-ments are made across different azimuths (i.e., the bars are different colors), the splitting characteristics willtend to differ also. For example, along the East Pacific Rise at 30°S, 110°W, there is a cluster of splitting mea-surements in both red and cyan (Figure 7b). Those in pink-reddish colors have northerly azimuths (recordedin North America) and tend to display NE-SW fast directions, in opposing orientation to those in cyan, whichhave southerly azimuths (recorded in Antarctica).

The complexities and intricacies of the azimuthal dependency become even more apparent if we focus onthe central Mid-Atlantic Ridge region where there is an abundance of splitting (Figure 7a). Again, similar col-ors and similar azimuths tend to produce similar fast directions and delay times, while different azimuthsshown by different colors give different results. At the eastern corner of the Romanche Transform (0°N,18°W, cluster #1 on Figure 7a) a gradual increase in the ray azimuth from 0 to 50° represented by pink-redto orange-yellow produces a clear rotation of the observed fast direction from ENE-WSW to NNE-SSW. It doesnot seem, however, that a universal azimuthal relationship exists as the pattern can change rapidly from oneridge segment to the next. For example, at the Doldrums Transforms (8°N, 35°W, cluster #2), the eastern

Figure 6. Comparison of source-side splitting results and SKS splitting from the Cascadia Initiative. Only offshore stations west of the trench are shown. (a) Stackedsplitting results from Bodmer et al. (2015) plotted as black bars. Our two source-side splits from the region are plotted in orange, corresponding to a NNE azimuth (seecolor scale from Figure 4). (b) The same as for (a) but comparing with individual split measurements of Martin-Short et al. (2015). (c) Number of individual nullmeasurements made at each station by Martin-Short et al. (2015) are represented by circles colored according to the scale below. Numbers of null source-sidemeasurements for three events (stars) near the Blanco Transform Fault are also shown.

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cluster of splits displays SE-NW magenta fast directions (azimuth ~300°) and NE-SW orange fast directions(azimuth ~45°). Meanwhile, less than 500 km to the west (cluster #3), the pattern is reversed, with magentafast directions now orientated NE-SW and orange fast directions SE-NW. We note that for both Doldrumsevents, each splitting measurement is made at a different station, and that the consistency in results forsimilar azimuths (e.g., for stations in North America with an azimuth of ~300°) is not due to the same receiver,but seen by multiple receivers, separated by considerable distance (Figure 2), but of similar azimuth. This isdemonstrated by the stereo-plots in Figure S7.

4. Discussion

Our source-side splitting analysis has revealed a complex pattern of anisotropy beneath the global system oftransform faults. It suggests the oceanic paradigm of azimuthal anisotropy aligned with seafloor spreading(Maggi et al., 2006; Montagner & Tanimoto, 1991; Nishimura & Forsyth, 1988) does not hold true within theimmediate vicinity of the plate boundary. This is not wholly unsurprising given that transform faults markthe dividing line between two opposing plate motions, and therefore two opposing mantle flow directionsthat must connect at the plate boundary.

We can, however, outline several key characteristics of our data set that any credible interpretation should beable to explain. First, and most importantly, the majority (60%) of our results are nulls. Second, the 60:40 ratiobetween split and null measurements is consistent across all spreading rates and does not vary with distancealong the transform (Figure 5). Third, the 40% splits display clear azimuthal dependence. Given that we donot expect much change in the focal mechanisms between similarly located events, the initial polarizationshould remain similar also. This means that the azimuthal dependence seen cannot be attributed to multiplelayers of anisotropy, as would typically be the case for SKS receiver-side splitting.

Bearing the above in mind, and our predominance of nulls, we first discuss the common ways in which nullmeasurements can be widely generated. First, a lack of coherent seismic anisotropy (i.e., mantle isotropy)could exist beneath transform faults. This could be due to strong heterogeneity (Eakin et al., 2015;Rümpker & Silver, 1998; Saltzer et al., 2000) or irregular mantle flow. While an isotropic upper mantle wouldarguably satisfy the majority of the results (60% nulls), deformation of the mantle is expected to be concen-trated near plate boundaries and so widespread isotropy beneath transform faults where a strong gradient inmantle deformation is required seems unlikely.

Second, nulls can be expected when the incoming polarization of the shear wave is aligned with the fast orslow direction (in the plane orthogonal to the raypath) (Savage, 1999), or for similar reasons when two aniso-tropic layers exist with a 90° difference in Φ between the layers (Eakin et al., 2015; Silver & Savage, 1994). Thesource polarization of event clusters should, however, be similar, given that focal mechanism does notchange along a single transform fault. This could potentially explain the nulls, given that a common relation-ship exists between the fault geometry, focal mechanism, and source polarization. If this were indeed occur-ring, however, then we would not expect to see nulls and splits for the same event or event cluster. This is

Figure 7. Regional case examples from the (a) central Mid-Atlantic Ridge and (b) East Pacific Rise illustrating the azimuthaldependence of source-side splitting results. The coloring and positioning of the bars is the same as Figure 4. Numbers (1, 2,and 3) refer to individual event clusters discussed in the text. Inset global map provides the locations of both Figures(red boxes).

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clearly not the case. As is seen in Figure 4, both nulls and splits are found together in most earthquakelocations. Such a mechanism is therefore not possible to explain the 60% nulls on a global scale, butlimited individual cases may exist.

Alternatively, the upper mantle could display a form of anisotropy with a near vertical symmetry axis, in whichcase velocities in the horizontal plane are similar in any direction but comparably faster or slower in the ver-tical direction (i.e., radial anisotropy). For a teleseismic shear wave with a steep angle of incidence (e.g., SKS),such a vertical symmetry axis would cause weak to no splitting, as the rays would travel close to the symmetryaxis. For our moderately inclined raypaths (inclination ~25°), this could generate a mix of nulls and splitting,

nullsplitting

)b()a(

)d()c(

)f()e(

0

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0

20

40

60

80

% N

ulls

acr

oss

360°

0

50

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t Dire

ctio

n (°

)

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ay T

ime

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100 200 300 100 200 300

Backazimuth of Raypath (°)

90° (vertical) 60° 30° 0° (hori)Fast axis dip:

20 40 60 80 0 20 40 60 80

SKSSource-side S

Figure 8. Predicted splitting parameters (a–d) for a 100 km thick layer of the upper mantle with a LPO of olivine (A-type:Karato, 2008). The left column shows predicted values across all azimuths for typical source-side ray geometries (avera-ge inclination = 24°), while on the right the same is shown for SKS geometries, which have steeper raypaths (inclina-tion = 10°). The different colors, as shown in legend, represent the dip angle between the modeled fast a axis of olivine(as in Figure S9) and the horizontal. The dashed black line (a and b) represents the dip direction. For (c and d), the greyshaded region and black dashed line signify the 0.5 second splitting cut-off. Predicted delay times smaller than this amount(dotted colored lines) are below the limit of detectability for teleseismic shear waves and would equate to a null mea-surement. Azimuth of the raypath as plotted on the x axis is the same quantity as that represented by the color wheel inFigure 4. Strong azimuthal dependence, and a mixture of splitting and nulls, appears when the fast axis of anisotropyapproaches vertical. For (e and f), the percentage of predicted nulls across all azimuths is shown as a function of fast axisdip. This is equivalent to how often the predicted delay time falls below the 0.5 second cut-off in (c and d) for a given dipangle. In (e) the percentage of nulls found by our source-side splitting data set (60%) is given by the dashed black line.

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as well as azimuthally varying splitting parameters, depending on thedip angle between the symmetry axis and the raypath. This is demon-strated in Figure 8 for a classic A-type olivine LPO example. The elasticconstants for the LPO fabric (Table S3) are derived from experimentson olivine aggregates under conditions typical of the upper mantle(see page 407 of (Karato, 2008)). Such A-type is characterized by a fastsymmetry axis whereby the fast a axes of the individual olivine crystalstend to align with the direction of shear (i.e., mantle flow direction)(Zhang & Karato, 1995). There are several other known types of olivineLPO fabrics but A-type is the most commonly found in natural samples,particularly for ridge peridotites (Michibayashi et al., 2016).

Taking our olivine LPO example, we therefore explore different dipangles of the fast symmetry axis by rotating the elastic tensor usingthe Matlab and Seismic Anisotropy Toolkit (Walker & Wookey, 2012)(Figure S7). For the complete 360° azimuthal rangewe can then calculatethe predicted splitting parameters for typical source-side and SKS rayinclinations by solving the Christoffel equation. This predicts the polari-

zation of the fast quasi-S wave as wells as the strength of the S wave anisotropy. By assuming a 100 km thicklayer of anisotropy we can then generate an estimate of the splitting delay time. A predicted delay time of lessthan 0.5 s is designated as a null measurement as this falls below the limit of detectability for teleseismic Swave frequencies, as evidenced by the minimum delay time recorded in our data set (Figures 5c and 5d).

From Figures 8c–8f it is seen that, in general, the tendency for null results increases (i.e., across a wider rangeof azimuths) as the dip angle of the fast symmetry axis steepens. For typical source-side ray inclinations, nullsare predicted for 60% of azimuths (as we find in our data set) when the fast axis is dipping around 75° fromthe horizontal. In addition, as the dip angle approaches vertical, the azimuthal variability in the splitting para-meters (Φ and δt) increases (Figures 8a–8d) and is more pronounced for source-side than for SKS. Even whenthe fast axis is vertical, it is still possible to generate some SKS splitting, which may explain, for example, whySKS splitting is found along the Blanco Transform Fault (Figure 6) when mantle upwelling has been otherwiseinferred in the same location (Byrnes et al., 2017). Conversely, when the fast axis is horizontal or shallowly dip-ping, no null measurements are expected for either SKS or source-side. A steeply dipping or near-vertical sym-metry axis of anisotropy would therefore be able to explain all the main characteristics of our data set,namely, a 60:40 ratio of nulls to splits with azimuthally dependent splitting. For A-type olivine fabric thiswould imply vertical mantle flow. Other mechanisms for producing anisotropy, such as shape-preferredorientations could also achieve a similar effect. If, for example, we consider a model for aligned melt inclu-sions (Figures S8 and S9 and Table S4 (Blackman & Kendall, 1997; Holtzman & Kendall, 2010; Murase &McBirney, 1973; Tandon & Weng, 1984)) that is characterized by a slow symmetry axis, then either a verticalor intermediate (~45–60°) dip of this symmetry axis could also generate 60% source-side nulls and azimuthalvariability in Φ and δt (Figure S8).

Given that there appears to be no clear relationship between the fast direction and the orientation of seafloorspreading (Figure 5a), we can only constrain the probable dip angle of an anisotropic symmetry axis, but notits dip direction (at least in the global sense). This may be possible on an event by event basis, but only ahandful of azimuths are ever sampled for a given event (Figure S7) meaning that any such attempt at mod-eling at present would be highly nonunique. Additionally, comparing event clusters 2 and 3 in Figure 6a, itappears that even for the same seafloor spreading and transform geometry the azimuthal pattern canchange rapidly between one transform segment to the next.

From the first order view of seafloor spreading it might be expected that oceanic transform faults should dis-play horizontal shear in the mantle parallel to the transform given the strike-slip motion along the fault.Numerical models of simplified ridge-transform systems (Behn et al., 2007; Morgan & Forsyth, 1988; Shen &Forsyth, 1992; Weatherley & Katz, 2010), however, tend to display mantle upwelling at asthenospheric depthsdirectly beneath the transform (Figure 9). In these models, vertical velocities tend to zero away from the plateboundary, are highest along the ridge axis, but display intermediate values beneath the transform fault. Thisvertical flow pattern appears to be induced to accommodate opposing plate motions on either side of the

Figure 9. Schematic interpretation of upper mantle deformation beneathtransform faults (TF) based on modeling of our source-side splitting results.A cross section along the transform fault is shown, perpendicular to theadjoining ridge (R) segments. The black bars representing mantle flow aremodified from the numerical modeling studies of Morgan & Forsyth (1988)and Behn et al. (2007), which suggest mantle upwelling directly beneathtransform faults. This is in agreement with our global catalogue of splittingresults measured from direct S rays with an average inclination of ~25°(blue arrows).

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plate boundary, with the horizontal differential motion gradually distributed over a zone surrounding thefault. The horizontal velocities themselves are typically small directly beneath the transform. It is worth notingthat this pattern is consistent for both passive (e.g., Morgan & Forsyth, 1988) and dynamic (i.e., buoyancy dri-ven) systems (e.g., Sparks et al., 1993), although the more complex buoyancy-driven flows may contribute toa lack of dependence on the spreading orientation. Additionally, the predicted anisotropy from both types ofsystems is consistent with near vertical alignment near the ridge (Blackman & Kendall, 2002), but a directcomparison along a transform fault has yet to be done. It is known, however, that geodynamical models thatincorporate a realistic viscoplastic rheology further enhance mantle upwelling beneath the oceanic trans-forms (Behn et al., 2007; Roland et al., 2010).

The pattern of our results, our predominance of nulls, and our suggestion of an olivine (A-type or similar) LPOfabric with a steep fast axis are therefore consistent with the predicted mantle flow field from geodynamicalmodels. At present, it is difficult to explicitly state, however, what the lateral extent of mantle upwellingbeneath transforms would need to be due to the depth dependency of the shear wave Fresnel zone (widthof sensitivity). Future modeling work to directly compare 3D geodynamical models of mantle flow with theobserved splitting along oceanic transforms should, however, hopefully provide an answer.

Alternatively, a steep or intermediate slow axis of symmetry from aligned melt pockets would also be con-sistent with our data set (Figure S8). It is unclear, however, whether such a uniform distribution of meltcould persist globally beneath the ridge-transform system, particularly across a range of spreading ratesand transform fault length scales. On the other hand, deformation of the upper mantle and the develop-ment of LPO should be fairly ubiquitous (e.g., Park & Levin, 2002). This is particularly true in the vicinity ofplate boundaries where strain between tectonic plates tends to be concentrated. It therefore seems thaton the whole the anisotropic signature seen beneath transform faults is more likely to be due to coherentmantle flow rather than well-organized partial melt. Additionally, recent evidence from Byrnes et al. (2017)has been found to support widespread mantle upwelling along the full length of the Blanco TransformFault (Figure 6), as inferred from low shear wave velocities tomographically imaged in the upper mantle,and in agreement with the predictions from geodynamical models.

MORs (spreading centers) are the primary locales for mantle upwelling and the associated production of newseafloor. The possibility of transform faults, however, as secondary narrow zones of upwelling has the poten-tial to explain several puzzling observations. For example, upwelling is likely to warm the fault, lowering theviscosity, and thus helping to maintain a zone of weakness (i.e., shear localization) that stabilizes the plateboundary (Behn et al., 2007; Bercovici, 2003). A warmer thermal profile is also able to better explain the depthdistribution of seismicity on oceanic transform faults (Behn et al., 2007; Roland et al., 2010) and may accountfor increasing evidence for sporadic magmatism along some transform faults, particularly fast slipping faultssuch as the Siqueiros on the East Pacific Rise that have been mapped at high resolution (Gregg et al., 2007).This phenomenon is also sometimes referred to as “leaky transforms” (Menard & Atwater, 1969). Upwelling ofthe mantle below transform faults would promote melting and may aid off-axis melt migration (Hebert &Montesi, 2011). This may encourage the development of intra-transform spreading centers, particularly dur-ing changes in plate motion (Fornari et al., 1989; Lonsdale, 1989). Further evidence for upwelling and meltingcan be found from negative residual mantle Bouguer gravity anomalies, from which partial crustal accretionalong some oceanic transform faults has been suggested (Gregg et al., 2007). This prompted Bai & Montési(2015) to demonstrate the ability to extract melt along fast spreading transforms when the melt permeabilitybarrier reaches a shallow depth.

It therefore appears that there is sufficient evidence to support mantle upwelling, at least to some extent,beneath transform faults globally, consistent with a near-vertical fast-axis of mantle anisotropy. As seen inFigure 9, the mantle flow pattern and resulting anisotropic structure is likely more complicated than wecan model for any given raypath at present. Even for one single transform fault, although the predictedflow pattern is generally steeply inclined, the dip varies in direction and inclination, becoming shallowercloser to the surface and toward the ridge segments. The evolution of anisotropy and its geometry willtherefore vary depending on the exact location of the earthquake along the fault and the azimuth ofthe raypath. In order to fully account for this in modeling regional specific mantle flow scenarios wouldbe needed for each fault. Other factors such as combined melting and LPO effects (Holtzman et al.,2003), as well as mantle serpentinization from seawater percolation into transform faults (Francis, 1981),could present likely added complications to the overall anisotropy signature.

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5. Conclusion

We have presented a new suite of source-side splitting observations that detail seismic anisotropy beneathtransform faults around the world. The pattern suggests an anisotropic geometry with a subvertical axis ofsymmetry, consistent with geodynamic models of mantle upwelling beneath oceanic transforms (Behnet al., 2007) and evidence of sporadic magmatism (Bai & Montési, 2015; Gregg et al., 2007). Such a scenarioimplies warming, and therefore weakening of transform faults, enhancing shear localization and long-termstability of divergent plate boundaries. Knock-on effects for heat flow, melt distribution, and global crustalproduction may also occur.

In order to fully understand the deformational processes along such plate boundaries, modeling studies withrealistic geometries are needed as well as targeted OBS studies to better illuminate the subsurface structure.Recent seismic deployments, such as the PILAB experiment over the equatorial Mid-Atlantic Ridge (www.pilabsoton.wordpress.com), are expected to deliver further insights in the near future. Overall, it is hoped thatour new global data set will provide much needed constraints for future investigations of MOR-TF dynamics.

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AcknowledgmentsThis work was undertaken as part of apostdoctoral fellowship jointly sup-ported by the Marine Geoscience groupat the National Oceanography CentreSouthampton and the Ocean and EarthScience department at the University ofSouthampton. Seismic data used in thisstudy were accessed through the DataManagement Centre (https://ds.iris.edu/ds/nodes/dmc/) of the IncorporatedResearch Institutions for Seismology(IRIS). We thank Robert Martin-Short forsharing a table of individual shearwaves splitting measurements for theCascadia Initiative. We acknowledgefunding from the Natural EnvironmentResearch Council (NE/M003507/1 andNE/K010654/1) and the EuropeanResearch Council (GA 638665). Weacknowledge helpful discussions withBram Murton and Mike Kendall in rela-tion to the dynamics and seismic sig-nature of seafloor spreading. We thanktwo anonymous reviewers for theirsuggestions and comments that helpedimprove the manuscript.

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